Snowflake GES-C01 - SnowPro® Specialty: Gen AI Certification Exam

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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A developer is building a prototype Gen AI application using the Snowflake Cortex LLM REST API. During testing, they frequently encounter HTTP 429 'Too Many Requests' errors, particularly when rapidly invoking the 'COMPLETE endpoint. Which of the following statements accurately describes the usage quotas and troubleshooting steps for such rate limit events in the Cortex REST API?

A) The HTTP 429 response indicates that the daily credit consumption for Snowflake Cortex AISQL usage has exceeded the 10-credit limit for on-demand accounts without a valid payment method, requiring conversion to a paid account.
B) Rate limits are model-specific, and exceeding either the Tokens Processed per Minute (TPM) or Requests per Minute (RPM) limits will result in an HTTP 429 response code.
C) Cortex REST API rate limits are primarily based on Requests per Minute (RPM) and do not consider the total Tokens Processed per Minute (TPM).
D) To resolve rate limiting, the developer should increase the virtual warehouse size for their API requests, as larger warehouses automatically increase API quotas.
E) If the usage is below the RPM limit but still triggers a 429, the developer should review the token usage rate as TPM limits might be exceeded.

2. An ML engineer is developing a RAG application in Python and wants to use the TruLens SDK to trace the distinct phases of its execution, specifically the context retrieval and answer generation steps. They aim to clearly differentiate the tracing of the function responsible for retrieving context.

A)

B)

C)

D)

E)

3. A data engineer is establishing a new Snowflake environment to support Document AI for processing incoming vendor invoices. They are setting up the necessary virtual warehouse, database, schema, and stages. Which of the following statements correctly identify essential considerations or requirements for this initial setup?

A) Any internal stages used for storing documents that will be processed by Document AI must explicitly enable SNOWFLAKE_SSE encryption.
B) The database and schema where Document AI model builds are created can be freely altered after creation, allowing for agile schema evolution.
C) Snowflake recommends using a large virtual warehouse, such as an 'L' or 'XL' size, to accommodate the intensive processing demands of Document AI and ensure high throughput.
D) A dedicated, smaller warehouse (e.g., 'X-SMALL', 'SMALL', or 'MEDIUM') should be created for Document AI to facilitate precise cost tracking, as scaling up warehouse size does not enhance Document AI query performance.
E) All documents intended for a single Document AI '!PREDICT operation must be stored within the same logical directory of the specified stage.

4.
Which 'combination of missing schema-level privileges' is explicitly cited in the documentation as a direct cause for this error, assuming a unique model build name?

A)

B)

C)

D)

E)

5. A Gen AI Specialist is developing a conversational analytics application using Cortex Analyst, aiming to provide a seamless multi-turn conversation experience for business users querying structured dat a. The team observes that follow-up questions are sometimes misinterpreted, especially when the conversation history is long. Which of the following statements accurately describe how Cortex Analyst handles multi-turn conversations and key considerations for optimizing this functionality?

A) Multi-turn conversation in Cortex Analyst is primarily handled by the CORTEX_ANALYST_MODEL AZURE_OPENAI parameter, which, when enabled, allows Azure OpenAl models to manage conversational context more effectively.
B) Cortex Analyst incorporates an additional LLM summarization agent before its original workflow to rewrite current-turn questions based on conversation history, with Llama 3.1 70B being a recommended model for this task due to its performance in evaluating summarization quality.
C) To address misinterpretation in long conversations, the max_tokens parameter for the Cortex Analyst REST API should be significantly increased to ensure the LLM receives the complete historical context without truncation.
D) When a user shifts intent frequently in a multi-turn conversation, Cortex Analyst automatically resets the conversation history to prevent misinterpretations and improve accuracy.
E) Cortex Analyst simply passes the entire conversation history to all subsequent LLM calls, and optimizing this requires manually truncating the array in messages the REST API request.

Solutions: